Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

Abstract Background Voice features have been suggested as objective markers of bipolar disorder (BD). Aims To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective...

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Autores principales:	Maria Faurholt-Jepsen, Darius Adam Rohani, Jonas Busk, Maj Vinberg, Jakob Eyvind Bardram, Lars Vedel Kessing
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Voice analysis Classification Random Forest Bipolar disorder openSMILE Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurophysiology and neuropsychology QP351-495
Acceso en línea:	https://doaj.org/article/7467cfcefa284ed5b3c0dee7965a92f4
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spelling	oai:doaj.org-article:7467cfcefa284ed5b3c0dee7965a92f42021-12-05T12:09:52ZVoice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states10.1186/s40345-021-00243-32194-7511https://doaj.org/article/7467cfcefa284ed5b3c0dee7965a92f42021-12-01T00:00:00Zhttps://doi.org/10.1186/s40345-021-00243-3https://doaj.org/toc/2194-7511Abstract Background Voice features have been suggested as objective markers of bipolar disorder (BD). Aims To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. Methods Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n = 78.733), UR (n = 8004), and HC (n = 20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. Results Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC = 0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC = 0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC = 0.67 (SD 0.11). Conclusions Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.Maria Faurholt-JepsenDarius Adam RohaniJonas BuskMaj VinbergJakob Eyvind BardramLars Vedel KessingSpringerOpenarticleVoice analysisClassificationRandom ForestBipolar disorderopenSMILENeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurophysiology and neuropsychologyQP351-495ENInternational Journal of Bipolar Disorders, Vol 9, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Voice analysis Classification Random Forest Bipolar disorder openSMILE Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurophysiology and neuropsychology QP351-495
spellingShingle	Voice analysis Classification Random Forest Bipolar disorder openSMILE Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurophysiology and neuropsychology QP351-495 Maria Faurholt-Jepsen Darius Adam Rohani Jonas Busk Maj Vinberg Jakob Eyvind Bardram Lars Vedel Kessing Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
description	Abstract Background Voice features have been suggested as objective markers of bipolar disorder (BD). Aims To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. Methods Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n = 78.733), UR (n = 8004), and HC (n = 20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. Results Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC = 0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC = 0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC = 0.67 (SD 0.11). Conclusions Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.
format	article
author	Maria Faurholt-Jepsen Darius Adam Rohani Jonas Busk Maj Vinberg Jakob Eyvind Bardram Lars Vedel Kessing
author_facet	Maria Faurholt-Jepsen Darius Adam Rohani Jonas Busk Maj Vinberg Jakob Eyvind Bardram Lars Vedel Kessing
author_sort	Maria Faurholt-Jepsen
title	Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_short	Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_full	Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_fullStr	Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_full_unstemmed	Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_sort	voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/7467cfcefa284ed5b3c0dee7965a92f4
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Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

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